What is the difference between policy and action in reinforcement learning?
A policy is a function that maps states to a probability distribution over all possible actions. 

So, in a typical Atari game, there might just be a handful of actions, represented by the keys that are used to play the game. In this context, the policy of a reinforcement learner might be represented by a pretty complex neural network that gets pixels as input and gives action probabilities as output.